ADEA Faculty Diversity Toolkit: A Comprehensive Approach to Improving Diversity and Inclusion in Dental Education
Bibliographic record
Abstract
Population demographic shifts in the United States and Canada have led to an increasingly diverse postsecondary student population. However, the largely homogenous dental faculty in the United States and Canada does not reflect the rapidly changing student body and the diverse patient population academic dentistry has been called to serve. Therefore, recruitment and retention of diverse dental faculty in dental education must be a priority. Substantial evidence also indicates improved outcomes for faculty, students, and institutions when faculty diversity on campus is increased. Beyond the positive impact faculty diversity can deliver to the learning and working environments of an academic institution, a variety of regulatory bodies mandate good faith efforts to maintain a diverse faculty, including the Commission on Dental Accreditation (CODA) standards for dental schools and dental therapy education programs. To assist its member institutions with answering the call for improved faculty diversity, the American Dental Education Association (ADEA) worked with its members to develop the ADEA Faculty Diversity Toolkit (ADEA FDT), a landmark evidence-based resource designed to assist dental education with the design and implementation of faculty recruitment and retention initiatives that can be tailored to their unique needs. This article provides an overview of the changing landscape of the United States and Canadian populations, shares the historic homogeneity of dental education faculty, provides an overview of some of the benefits associated with faculty diversity and highlights the challenges and barriers related to recruiting and retaining diverse faculty. Most importantly, it introduces the ADEA FDT and the need for dental schools and allied dental programs to use the Toolkit as a proactive resource in increasing and maintaining faculty diversity. Furthermore, it provides an overview of how to utilize and adapt the highlighted best practices and model programs to improve faculty diversity on their campuses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.042 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".